Optimal parameters setting of injection moulding (IM) machine critically effects productivity, quality, and cost production of end products in manufacturing industries. Previously, trial and error method were the most common method for the production engineers to meet the optimal process injection moulding parameter setting. Inappropriate injection moulding machine parameter settings can lead to poor production and quality of a product. Therefore, this study was purposefully carried out to overcome those uncertainty. This paper presents a statistical technique on the optimization of injection moulding process parameters through central composite design (CCD). In this study, an understanding of the injection moulding process and consequently its optimization is carried out by CCD based on three parameters (melt temperature, packing pressure, and cooling time) which influence the shrinkage and tensile strength of rice husk (RH) reinforced low density polyethylene (LDPE) composites. Statistical results and analysis are used to provide better interpretation of the experiment. The models are form from analysis of variance (ANOVA) method and the model passed the tests for normality and independence assumptions.

Dolomite can be used as a source of aluminosilicate to produce geopolymers; however, this approach is limited by its low reactivity. This study analyzes the viability of producing geopolymers using dolomite/fly-ash with sodium silicate and NaOH solutions (at multiple concentrations) by determining the resultant geopolymers’ compressive strengths. The dolomite/fly-ash-based geopolymers at a NaOH concentration of ~22 M resulted in an optimum compressive strength of 46.38 MPa after being cured for 28 days, and the SEM and FTIR analyses confirmed the denser surface of the geopolymer matrix. The synchrotron micro-XRF analyses confirmed that the Ca concentration exceeded that of Si and Mg, leading to the formation of calcium silicate hydrate, which strengthens the resulting geopolymers.